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Design Calculations for a Hazard Ratio Estimand

Source: R/hr_design.R
hr_design.Rd

This is a function for designing a study with a time-to-event outcome whose inferential target is the hazard ratio. hr_design() will calculate the minimum number of events, minimum hazard ratio, power, or Type I error when the other arguments are specified.

Usage

hr_events(hazard_ratio, power = 0.8, alpha = 0.05, test_sides = 2, ratio = 1)

hr_power(events, hazard_ratio, alpha = 0.05, test_sides = 2, ratio = 1)

hr_alpha(events, hazard_ratio, power = 0.8, test_sides = 2, ratio = 1)

hr_minimal(events, power = 0.8, alpha = 0.05, test_sides = 2, ratio = 1)

hr_design(
  events = NULL,
  hazard_ratio = NULL,
  power = NULL,
  alpha = NULL,
  test_sides = 2,
  ratio = 1
)

Arguments

hazard_ratio

A numeric vector: the minimum hazard ratio of interest

power

A numeric vector: the statistical power of interest

alpha

A numeric vector: Type I Error probability

test_sides

A numeric vector: Number of sides for test (1 or 2)

ratio

A numeric vector: the allocation ratio of treatment to control, i.e. for an \(r:1\) trial, ratio = \(r\).

events

A numeric vector: the number of observed events

Value

a numeric vector or data.frame, depending on whether arguments are specified as scalars or vectors.

Details

The function hr_design takes in user input, and depending on the arguments supplied, calls the appropriate function depending on whether the number of pooled events observed (hr_events), power (hr_power), Type I Error (hr_alpha), or relative risk (hr_minimal) is left unspecified.

References

Schoenfeld, DA. 1983. "Sample-Size Formula for the Proportional-Hazards Regression Model." Biometrics 39 (2): 499. https://doi.org/10.2307/2531021.

See also

stats::power.t.test() for design calculations for a difference in means, stats::power.prop.test() for design calculations for a difference in proportions, rr_design() for design calculations for a relative risk estimand.

Examples

# Number of Events
hr_design(
  hazard_ratio = 0.75,
  power = 0.80,
  alpha = 0.05,
  test_sides = 2
)
#> [1] 379.3517

# Power
hr_design(
  events = 400,
  hazard_ratio = 0.75,
  alpha = 0.05,
  test_sides = 2
)
#> [1] 0.8203911

# Type I Error
hr_design(
  events = 400,
  hazard_ratio = 0.75,
  power = 0.80,
  test_sides = 2
)
#> [1] 0.0418308

# Hazard Ratio
hr_design(
  events = 400,
  power = 0.80,
  alpha = 0.05,
  test_sides = 2
)
#>   hazard_ratio hazard_ratio_inv
#> 1    0.7556639          1.32334